CN110244750B - Unmanned aerial vehicle tour path planning method and device - Google Patents
Unmanned aerial vehicle tour path planning method and device Download PDFInfo
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Abstract
The invention discloses a method and a device for planning an unmanned aerial vehicle tour path, wherein the space between two adjacent towers is divided into a plurality of grids through the position coordinates of the two adjacent towers; calculating grids where the tree barrier points are located according to the tree barrier point coordinates; and then, correcting the inspection path of the unmanned aerial vehicle according to the grid where the tree obstacle points are located, effectively avoiding the influence of the tree obstacle points in the area between two towers on the flight path of the unmanned aerial vehicle, considering the influence factors of the unmanned aerial vehicle when the power grid inspection flight task is executed under different working conditions, and improving the working efficiency of the power inspection operation system.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle inspection path planning method and device.
Background
In the electric power facility system, a strip-shaped area under a line extending to both sides by a predetermined width along a high-voltage overhead power line roadside conductor is called a power transmission line corridor. In a traditional transmission line passage corridor, when the distance between a tree and a line wire is lower than a safe distance, the wire breaks down air, and a grounding loop is formed with the tree and the ground, so that the transmission line is tripped. The resulting transmission line tripping is called a "tree fault", simply referred to as a tree barrier. The tree barrier easily causes line outage and is very harmful. Running experience shows that timely control of the distance between the wire and the ground is significant in reducing the occurrence of tree barriers effectively. With the development of unmanned aerial vehicle technology and photogrammetry technology, the ways of utilizing unmanned aerial vehicles to patrol the transmission line corridor are increasing. When the unmanned aerial vehicle is utilized to carry out the line inspection, the safety distance of the unmanned aerial vehicle to the power transmission equipment is considered, the influence of the camera focal length, resolution, frame factors and the like on the image shooting quality is considered, and the problems of the unmanned aerial vehicle holder angle, the unmanned aerial vehicle head orientation and the like are solved. At the same time, the influence of unstable air flow and wind direction in the line inspection process is considered.
However, the traditional multi-rotor unmanned aerial vehicle inspection method is that inspection personnel manually control an aircraft to inspect on the ground, inspection quality and safety are limited by the operation level of a control hand, and the operation efficiency is low and the precision is low. When the unmanned aerial vehicle executes the power grid patrol aviation flight task, if aviation flight is interrupted due to an emergency or the unmanned aerial vehicle is damaged due to collision with a tree obstacle point, huge loss can be brought to patrol operation.
Disclosure of Invention
Aiming at the problems in the background technology, the unmanned aerial vehicle inspection path planning method and device are provided, constraint conditions and path planning methods of the unmanned aerial vehicle in the line corridor inspection can be researched according to the task requirements of the line corridor inspection and the unmanned aerial vehicle performance parameters, and a path planning mathematical model and an objective function are established, so that operation faults of inspection operation can be effectively prevented.
The invention discloses a method for planning an unmanned aerial vehicle tour path, which comprises the following steps:
s1, acquiring position coordinates of two adjacent towers, and dividing a space between the two adjacent towers into a plurality of grids according to the position coordinates of the towers;
s2, calculating grids of the obtained tree obstacle points according to the cloud coordinates of the tree obstacle points obtained by the multi-time unmanned aerial vehicle inspection;
s3, if the cloud coordinates of the tree barrier points obtained by multiple times of calculation are in the same grid, judging that the tree barrier points obtained by multiple times of calculation are the same tree barrier point;
s4, acquiring a patrol path of the unmanned aerial vehicle;
s5, correcting the unmanned aerial vehicle inspection path according to the unmanned aerial vehicle inspection path and the tree obstacle points.
The tower is a support in overhead transmission lines for supporting the transmission line. The tower is mostly made of steel or reinforced concrete, and is a main supporting structure of the overhead transmission line.
According to the invention, the tower coordinates and the tree obstacle point coordinates in the power transmission line are collected, the inspection path is planned according to the performance parameters of the unmanned aerial vehicle, and the inspection path is corrected through gridding treatment, so that the influence of the tree obstacle points in the area between two towers on the flight path of the unmanned aerial vehicle is effectively avoided, the influence factors of the unmanned aerial vehicle when the power grid inspection flight task is executed under different working conditions are considered, and the working efficiency of the power inspection operation system is improved.
Further, in the step of calculating the grid where the tree barrier points are located according to the tree barrier point coordinates, the size of the grid is 3 m by 3 m.
Further, the steps of acquiring the inspection path of the unmanned aerial vehicle include:
planning a patrol path between towers according to the performance parameters of the unmanned aerial vehicle and the patrol task of the circuit corridor;
solving the maximum path node number, the minimum step length and the maximum path deflection angle according to the unmanned aerial vehicle performance parameter data;
establishing a patrol path evaluation function corresponding to the line corridor patrol task data;
and solving the optimal value of the patrol path evaluation function through a genetic algorithm.
The inspection path of the unmanned aerial vehicle is that an optimal unmanned aerial vehicle flight path is obtained in the transmission line detection process, the unmanned aerial vehicle flight path takes off from a preset starting point, passes through a target point as many times as possible, and finally reaches the terminal point to finish the inspection task.
The path nodes are discretized in the flight path and space of unmanned aerial vehicle inspection, and the flight space and the inspection path are expressed by a series of path nodes.
The minimum step length refers to the minimum step length of the unmanned aerial vehicle, which is the minimum value of a distance required to be flown directly for overcoming the influence of inertia when the current flight attitude of the unmanned aerial vehicle needs to be changed.
The path deflection angle refers to the magnitude of the azimuthal deflection of the current flight path segment relative to the previous path segment. Because of the limitation of the maneuvering performance of the unmanned aerial vehicle, the path deflection angle is required to be in the range of the maximum path deflection angle and the opposite value of the maximum path deflection angle.
The genetic algorithm comprises a chromosome coding mode, an fitness function, a genetic operator and a termination criterion algorithm.
Specifically, the route corridor patrol task includes: latitude and longitude position information of a flying spot, a landing spot and a target point of the operation base.
Further, the step of solving the maximum path node number according to the unmanned aerial vehicle performance parameter data comprises the following steps: the maximum course of the unmanned plane in the tour process is marked as Vmax, n nodes are arranged in the course, the course of the ith section in the course is Vi, and the total course V of the tour course needs to meet the requirementThe maximum number of path nodes n can be solved.
Further, the step of solving the minimum step length according to the unmanned aerial vehicle performance parameter data comprises the following steps: the minimum distance of the unmanned aerial vehicle which is directly flown due to inertia when changing from the current flight state is recorded as the minimum step length L min Let the previous route point be S 0 The current path point is S 1 At S 1 Is the origin, the minimum step length L min The next candidate path point existing in the circle for radius does not satisfy the node selection principle, and the path point outside the circle is selected as the next path point S 2 。
Further, the step of solving the maximum path deflection angle according to the unmanned aerial vehicle performance parameter data comprises the following steps: defining the azimuth deflection of the current flight path segment relative to the previous path segment as a path deflection angle, setting delta phi max The maximum path deflection angle is calculated as follows: Δφ max ＝arcsin(L min /(2*r min ) A) is provided; wherein r is min For minimum turning radius, L min For minimum step size, r min The calculation formula of (2) is as follows:in n ymax For the maximum normal overload of the unmanned aerial vehicle, V is the current speed of the unmanned aerial vehicle, and g is a gravitational acceleration constant.
Further, establishing a patrol path evaluation function corresponding to the line corridor patrol task data, wherein the steps include: on inspectionA plurality of path points are designed in the task space, and any two adjacent path points are connected by a line segment to form a patrol path; each tour path is composed of a group of node sequences { S, D } 1 ，D 2 ，…，D n-1 E, where S is the starting point, E is the ending point, D 1 ，D 2 ，…，D n-1 Is an intermediate path node; setting a patrol path comprising n+1 target points, wherein pi is the length value of the ith section of the path, the starting point is the flying spot of the unmanned aerial vehicle operation base, and the patrol path evaluation function is as follows:where h is a penalty function, the minimum of the objective function is required.
Further, the method solves the optimal value of the inspection path evaluation function through a genetic algorithm, and comprises the following steps:
defining the starting point of the tour path as an origin, connecting the ending point with the starting point as a polar axis, and marking the coordinates of the starting point and the ending point as (0, 0) and rho respectively T ，θ T ) Dividing the inspection path into N sections according to the minimum step Lmin, and ensuring the radius rho of the first circle when N is selected 0 >Lmin，ρ T For the distance from the start point to the end point, then n=ρ T /ρ 0 . T1 and T2 represent target points;
the polar angle of the first waypoint is the deflection angle of the initial track section relative to the polar axis, θ1= delta phi 1;
from the polar angle θ1 and the deflection angle ΔΦ1, the polar angle θ2 is calculated:according to the polar angles thetai-2, thetai-1 and the path azimuth deflection angle delta phi i, the polar angle thetai (3 is less than or equal to i is less than or equal to n-1) is iteratively solved, and the polar angle thetai can be obtained
Wherein:
solving the deflection angle delta phi n of the end path
The invention also provides an unmanned aerial vehicle tour path planning device, which comprises:
means for acquiring position coordinates of two adjacent towers, dividing a space between the two adjacent towers into a plurality of grids according to the position coordinates of the towers;
the device is used for calculating grids where the tree barrier points are located according to the tree barrier point cloud coordinates obtained through multiple unmanned aerial vehicle inspection;
means for determining whether the tree barrier points obtained by the plurality of calculations are the same tree barrier point;
a device for acquiring a patrol path of the unmanned aerial vehicle;
the device is used for correcting the unmanned aerial vehicle inspection path according to the unmanned aerial vehicle inspection path and the tree obstacle point.
In order that the invention may be more clearly understood, specific embodiments thereof will be described below with reference to the accompanying drawings.
Drawings
Fig. 1 is a flowchart of a method for planning an inspection path of an unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a total course track according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of minimum step node selection according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a maximum path deflection angle according to an embodiment of the present invention.
Fig. 5 is a schematic diagram illustrating calculation of a deflection angle of a routing inspection path according to an embodiment of the present invention.
FIG. 6 is a polar angle solution schematic of an embodiment of the present invention.
Fig. 7 is a schematic diagram of a node sequence expression routing inspection path according to an embodiment of the present invention.
Detailed Description
Fig. 1 is a flowchart of a method for planning a patrol path of an unmanned aerial vehicle according to an embodiment of the invention.
The invention discloses a method for planning an unmanned aerial vehicle tour path, which comprises the following steps:
s1, acquiring position coordinates of two adjacent towers, and dividing a space between the two adjacent towers into a plurality of grids according to the position coordinates of the towers;
s2, calculating grids of the obtained tree obstacle points according to the cloud coordinates of the tree obstacle points obtained by the multi-time unmanned aerial vehicle inspection;
s3, if the cloud coordinates of the tree barrier points obtained by multiple times of calculation are in the same grid, judging that the tree barrier points obtained by multiple times of calculation are the same tree barrier point;
s4, acquiring a patrol path of the unmanned aerial vehicle;
s5, correcting the unmanned aerial vehicle inspection path according to the unmanned aerial vehicle inspection path and the tree obstacle points.
According to the invention, the tower coordinates and the tree obstacle point coordinates in the power transmission line are collected, the inspection path is planned according to the performance parameters of the unmanned aerial vehicle, and the inspection path is corrected through gridding treatment, so that the influence of the tree obstacle points in the area between two towers on the flight path of the unmanned aerial vehicle is effectively avoided, the influence factors of the unmanned aerial vehicle when the power grid inspection flight task is executed under different working conditions are considered, and the working efficiency of the power inspection operation system is improved.
The flight path and space of unmanned aerial vehicle inspection are continuous, and discretization processing is needed when path planning is carried out. Dividing the path space, namely representing the search space by establishing an expression or a data structure; secondly, establishing a line patrol path evaluation function corresponding to the search space; the established optimal value of the evaluation function is then solved by means of an algorithm. In order to ensure that the planned path has feasibility, the path planning needs to meet various constraint conditions such as inspection tasks and unmanned aerial vehicle performance parameters.
The unmanned aerial vehicle tour path planning method of the present invention is described below in one embodiment.
In this embodiment:
the step of obtaining the position coordinates of two adjacent towers and the coordinates of the tree barrier points comprises the following steps:
acquiring three-dimensional laser point cloud data of a power transmission line;
and dividing the three-dimensional laser point cloud data into wire point cloud, pole tower point cloud and ground object point cloud by using a point cloud classification technology.
Specifically, the three-dimensional laser point cloud data of the power transmission line are obtained by using unmanned aerial vehicle-mounted laser radar equipment, and the power transmission line point cloud data are classified into wire point cloud, pole tower point cloud and ground object point cloud after being collected.
The point cloud data is recorded in the form of points of the scanning data, and each point comprises three-dimensional coordinates, and some points may contain color information or reflection intensity information.
The point cloud classification technology is to separate ground points from non-ground points according to a point cloud filtering algorithm, and then automatically extract ground features such as power towers, power lines and vegetation in original point cloud data in sequence.
The wire point cloud is point cloud data on a power line; the tower point cloud is point cloud data on the electric power tower, namely tower position coordinate information required by the embodiment; the ground object point cloud is point cloud data on ground objects such as ground vegetation, namely tree barrier point coordinate information required by the embodiment.
In step S1, dividing the space between two adjacent towers into a plurality of grids; positioning the tree barrier points detected in multiple periods in multiple areas, and calculating grids where the tree barrier points are located each time according to the tree barrier point coordinates obtained by multiple unmanned aerial vehicle inspection; and if the tree barrier points obtained by multiple times of calculation are in the same grid, judging that the tree barrier points obtained by multiple times of calculation are the same tree barrier point.
In this embodiment, the size of the grid is 3 meters by 3 meters. In other alternative embodiments, the size of the grid is determined according to a preset correction accuracy.
Please refer to fig. 2, which is a schematic diagram of a general course of the present invention.
The maximum course of the unmanned plane in the tour process is marked as Vmax, n nodes are arranged in the course, the course of the ith section in the course is Vi, and the total course V of the tour course needs to meet the requirement
The maximum number of path nodes n can be solved.
In addition to the maximum range constrained by the overall range trajectory schematic, the unmanned aerial vehicle must also meet constraints of minimum step size and maximum path deflection angle.
Please refer to fig. 3, which is a schematic diagram illustrating a minimum step node selection according to an embodiment of the present invention.
When the current flight attitude of the unmanned aerial vehicle needs to be changed, the unmanned aerial vehicle also needs to fly directly for a distance to overcome the influence of inertia, and the minimum value of the distance is called as the minimum step length and is marked as L min Let the previous route point be S 0 ，S 1 S is the current path point 2 ，S 3 ，S 4 ，S 5 ，S 6 And S is 7 For the next path node to be selected, the corresponding step length is L 1 ，L 2 ，L 3 ，L 4 ，L 5 And L 6 Are not satisfied because they are smaller than the minimum step length L min And small, so that the next path node can only be selected among the four.
Please refer to fig. 4, which is a schematic diagram of a maximum path deflection angle according to an embodiment of the present invention.
Defining the azimuth deflection of the current flight path section relative to the previous path section as a path deflection angle, wherein the path deflection angle is limited by the maneuverability of the unmanned aerial vehicle and needs to meet-delta phi max ≤Δφ i ≤Δφ max Let DeltaPhi be max The maximum path deflection angle is calculated as follows: Δφ max ＝arcsin(L min /(2*r min ) A) is provided; wherein r is min For minimum turning radius, L min For minimum step size, r min The calculation formula of (2) is as follows:in n ymax For the maximum normal overload of the unmanned aerial vehicle, V is the current speed of the unmanned aerial vehicle, and g is a gravitational acceleration constant.
Fig. 5 is a schematic diagram illustrating calculation of a deflection angle of a routing inspection path according to an embodiment of the invention.
In this embodiment, a polar coordinate encoding mode is selected, a starting point of the inspection path is defined as an origin, a connection line between a termination point and the starting point is defined as a polar axis, and coordinates of the starting point and the termination point are respectively recorded as (0, 0), (ρ) T ，θ T ) Dividing the inspection path into N sections according to the minimum step Lmin, and ensuring the radius rho of the first circle when N is selected 0 >Lmin，ρ T For the distance from the start point to the end point, then n=ρ T /ρ 0 。T 1 、T 2 Representing a target point;
the azimuthal offset of the current flight path segment relative to the previous path segment is the path deflection angle,for the deflection angle of the first path segment (O.fwdarw.1) from the polar axis, which is also the polar angle of path segment 1, +.>The deflection angle for the path segment (i-1- > i) is offset from the deflection angle for the previous path segment (i-2- > i-1). /> Is the deflection angle of the last path segment (N-1- > T) relative to the path segment (N-1- > N-2).
3 constraint conditions in unmanned aerial vehicle inspection path planning can be well solved when the coding mode is adopted: (1) the maximum path deflection angle constraint can be solved during chromosome coding; (2) selecting an appropriate ρ 0 Solving the constraint of the minimum step length by the value; (3) the constraint of the maximum path node number is solved by the length of the track chromosome.
Please refer to fig. 6, which is a polar angle solving diagram according to an embodiment of the present invention.
The polar angle of the first waypoint is the deflection angle of the initial track segment relative to the polar axis, θ 1 ＝△
From polar angle theta 1 And deflection angleFind the polar angle θ 2 ：/>According to polar angle theta i-2 ，θ i-1 Path azimuth deflection angle->Iterative solution of polar angle θ i (3.ltoreq.i.ltoreq.n-1) to obtain
Wherein:
solving the deflection angle delta phi n of the end path
Please refer to fig. 7, which is a schematic diagram of a node sequence expression inspection path according to an embodiment of the present invention.
Designing a plurality of path points in the patrol task space, and connecting any two adjacent path points by using line segments to form a patrol path; each tour path is composed of a group of node sequences { S, D } 1 ，D 2 ，…，D n-1 E, where S is the starting point, E is the ending point, D 1 ，D 2 ，…，D n-1 Is an intermediate roadAnd (5) a diameter node. Setting a patrol path comprising n+1 target points, p i For the length value of the ith section of the path, the starting point is the flying spot of the unmanned aerial vehicle operation base, and the inspection path evaluation function is as follows:where h is a penalty function, the minimum of the objective function is required.
Further, according to the grid through which the unmanned aerial vehicle inspection path passes and the grid in which the tree barrier point is located, the unmanned aerial vehicle inspection path is corrected, and the method comprises the following steps: judging whether grids where node coordinates in the patrol path are located overlap with grids where tree barrier points in a grid area between the two towers are located, so that nodes in the patrol path avoid the tree barrier points, and obtaining a line corridor patrol optimal path.
According to the method, constraint conditions and a path planning method of the unmanned aerial vehicle in the route corridor inspection are researched according to the task requirements of the route corridor inspection and the performance parameters of the unmanned aerial vehicle, and a path planning mathematical model and an objective function are built. The polar coordinate coding mode is designed to solve the limitation of the maximum path deflection angle, the minimum step length and the maximum path node number, and a genetic algorithm is adopted to solve the unmanned aerial vehicle circuit corridor inspection path so as to obtain the optimal path for unmanned aerial vehicle power transmission corridor inspection.
In the traditional process of inspecting a power transmission line corridor by using an unmanned aerial vehicle, a common inspection path planning method is to calculate flight path points point by an operator, then manually input the data into a navigation system of the unmanned aerial vehicle, and the calculation requirement is extremely accurate, and if errors occur in the calculation, potential safety hazards are brought, so that the method is a very heavy task. The unmanned aerial vehicle ground control system is popularized along with the development of a computer GPS technology, and workers carry out unmanned aerial vehicle line inspection shooting path planning by using the system and directly transfer the unmanned aerial vehicle line inspection shooting path planning to a digital map. Therefore, the planning time of the inspection shooting path is greatly shortened, and the flight position of the unmanned aerial vehicle can be tracked and acquired in real time.
According to the invention, the number of the path nodes can be reasonably adjusted according to the expected precision by processing the flight path and the space grid of the unmanned aerial vehicle inspection, the initial planning problem is divided into a plurality of sub-problems, the constraint condition is converted into the expression mode which can be identified by the computer, the complexity of the problem is reduced, a series of path points are used for representing the corridor inspection path planning problem, and the calculation and the expression are convenient. Meanwhile, based on comparison of the inspection multi-period data of the unmanned aerial vehicle, the area where the ground object or the tree under the lead changes can be found, coordinate information of the tree barrier points is confirmed, when the inspection route of the unmanned aerial vehicle is updated, the positions of all the tree barrier points are avoided, loss caused by collision of the unmanned aerial vehicle in the flight process is prevented, and references are provided for line corridor management and control.
Compared with the prior art, the method adopts basic ideas and concepts of genetic algorithm, combines actual requirements of inspection of the transmission line unmanned aerial vehicle transmission corridor, selects proper fitness function and genetic operator, adopts a chromosome coding mode based on the maximum path deflection angle, and is used for solving constraints of unmanned aerial vehicle path deflection angle and minimum step length in path rules. Therefore, an optimal path for the unmanned aerial vehicle to carry out the route corridor inspection is obtained.
The present invention is not limited to the above-described embodiments, and if various modifications or variations of the present invention are not departing from the spirit and scope of the present invention, the present invention also includes such modifications and variations provided they fall within the scope of the claims and the equivalents thereof.
Claims (8)
1. An unmanned aerial vehicle tour path planning method comprises the following steps:
acquiring position coordinates of two adjacent towers, and dividing a space between the two adjacent towers into a plurality of grids according to the position coordinates of the towers;
according to the cloud coordinates of the tree barrier points obtained by the multi-time unmanned aerial vehicle inspection, calculating grids of the tree barrier points obtained each time;
if the cloud coordinates of the tree barrier points obtained by multiple times of calculation are in the same grid, judging that the tree barrier points obtained by multiple times of calculation are the same tree barrier point;
acquiring a patrol path of the unmanned aerial vehicle;
wherein, obtain unmanned aerial vehicle's route of patrolling and examining includes: planning a patrol path between towers according to the performance parameters of the unmanned aerial vehicle and the patrol task of the circuit corridor;
solving the maximum path node number, the minimum step length and the maximum path deflection angle according to the unmanned aerial vehicle performance parameter data;
establishing a patrol path evaluation function corresponding to the line corridor patrol task data;
solving an optimal value of the patrol path evaluation function through a genetic algorithm;
wherein the solving the optimal value of the patrol path evaluation function by the genetic algorithm includes: defining the starting point of the tour path as an origin, connecting the ending point with the starting point as a polar axis, and marking the coordinates of the starting point and the ending point as (0, 0) and rho respectively T ，θ T ) Dividing the inspection path into N sections according to the minimum step Lmin, and ensuring the radius rho of the first circle when N is selected 0 >Lmin，ρ T For the distance from the start point to the end point, then n=ρ T /ρ 0 The method comprises the steps of carrying out a first treatment on the surface of the T1 and T2 represent target points;
the polar angle of the first waypoint is the deflection angle of the initial track segment relative to the polar axis, θ1=ΔΦ1;
from the polar angle θ1 and the deflection angle ΔΦ2, the polar angle θ2 is calculated:
according to the polar angles thetai-2, thetai-1 and the path azimuth deflection angle delta phi i, the polar angle thetai (3 is less than or equal to i is less than or equal to n-1) is iteratively solved, and the polar angle thetai can be obtained
Wherein:
solving the deflection angle delta phi n of the end path
Judging whether grids where node coordinates in the patrol path are located overlap with grids where tree barrier points in a grid area between the two towers are located, so that nodes in the patrol path avoid the tree barrier points, and obtaining a line corridor patrol optimal path.
2. The unmanned aerial vehicle tour path planning method according to claim 1, wherein in the step of calculating a grid where the tree obstacle point is located according to the tree obstacle point coordinates, the size of the grid is 3 m by 3 m.
3. The unmanned aerial vehicle tour path planning method of claim 1, wherein the route corridor tour task data comprises:
latitude and longitude position information of a flying spot, a landing spot and a target point of the operation base.
4. The unmanned aerial vehicle tour path planning method according to claim 1, wherein the step of solving the maximum path node number from the unmanned aerial vehicle performance parameter data comprises:
the maximum course of the unmanned plane in the tour process is marked as Vmax, n nodes are arranged in the course, the course of the ith section in the course is Vi, and the total course V of the tour course needs to meet the requirementThe maximum number of path nodes n can be solved.
5. The unmanned aerial vehicle tour path planning method according to claim 1, wherein: the step of solving the minimum step length according to the unmanned aerial vehicle performance parameter data comprises the following steps:
the minimum distance of the unmanned aerial vehicle which is directly flown due to inertia when changing from the current flight state is recorded as the minimum step length L min Let the previous route point be S 0 The current path point is S 1 At S 1 Is the origin, the minimum step length L min The next candidate path point existing in the circle for radius does not satisfy the node selection principle, and the path point outside the circle is selected as the next path point S 2 。
6. The unmanned aerial vehicle tour path planning method of claim 1, wherein the step of solving a maximum path deflection angle from the unmanned aerial vehicle performance parameter data comprises:
defining the azimuth deflection of the current flight path segment relative to the previous path segment as a path deflection angle, setting delta phi max The maximum path deflection angle is calculated as follows: Δφ max ＝arcsin(L min /(2*r min ) A) is provided; wherein r is min For minimum turning radius, L min For minimum step size, r min The calculation formula of (2) is as follows:in n ymax For the maximum normal overload of the unmanned aerial vehicle, V is the current speed of the unmanned aerial vehicle, and g is a gravitational acceleration constant.
7. The unmanned aerial vehicle tour path planning method according to claim 1, wherein establishing a tour path evaluation function corresponding to the line corridor tour task data includes:
designing a plurality of path points in the patrol task space, and connecting any two adjacent path points by using line segments to form a patrol path; each tour path is composed of a group of node sequences { S, D } 1 ，D 2 ，...，D n-1 E, where S is the starting point, E is the ending point, D 1 ，D 2 ，...，D n-1 Is an intermediate path node; setting a patrol path comprising n+1 target points, wherein pi is the length value of the ith section of the path, the starting point is the flying spot of the unmanned aerial vehicle operation base, and the patrol path evaluation function is as follows:where h is a penalty function, the minimum of the objective function is required.
8. An unmanned aerial vehicle tour path planning device, comprising:
means for acquiring position coordinates of two adjacent towers, dividing a space between the two adjacent towers into a plurality of grids according to the position coordinates of the towers;
the device is used for calculating grids where the tree barrier points are located according to the tree barrier point cloud coordinates obtained through multiple unmanned aerial vehicle inspection;
means for determining whether the tree-barrier points obtained by the multiple calculations are the same tree-barrier point;
a device for obtaining unmanned aerial vehicle's route of patrolling and examining, wherein, obtain unmanned aerial vehicle's route of patrolling and examining includes: planning a patrol path between towers according to the performance parameters of the unmanned aerial vehicle and the patrol task of the circuit corridor;
solving the maximum path node number, the minimum step length and the maximum path deflection angle according to the unmanned aerial vehicle performance parameter data;
establishing a patrol path evaluation function corresponding to the line corridor patrol task data;
solving an optimal value of the patrol path evaluation function through a genetic algorithm;
wherein the solving the optimal value of the patrol path evaluation function by the genetic algorithm includes: defining the starting point of the tour path as an origin, connecting the ending point with the starting point as a polar axis, and marking the coordinates of the starting point and the ending point as (0, 0) and rho respectively T ，θ T ) Dividing the inspection path into N sections according to the minimum step Lmin, and ensuring the radius rho of the first circle when N is selected 0 >Lmin，ρ T For the distance from the start point to the end point, then n=ρ T /ρ 0 The method comprises the steps of carrying out a first treatment on the surface of the T1 and T2 represent target points;
the polar angle of the first waypoint is the deflection angle of the initial track segment relative to the polar axis, θ1=ΔΦ1;
from the polar angle θ1 and the deflection angle Δφ2, the polar angle θ2 is determined：
According to the polar angles thetai-2, thetai-1 and the path azimuth deflection angle delta phi i, the polar angle thetai (3 is less than or equal to i is less than or equal to n-1) is iteratively solved, and the polar angle thetai can be obtained
Wherein:
solving the deflection angle delta phi n of the end path
And the device is used for judging whether the grid where the node coordinates in the inspection path are positioned overlaps with the grid where the tree barrier points in the grid area between the two towers are positioned, so that the nodes in the inspection path avoid the tree barrier points, and the optimal path for the line corridor inspection is obtained.
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